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An Efficient Algorithm for Connected Attribute Thinnings and Thickenings

  • David Lesage
  • Jérôme Darbon
  • Ceyhun Burak Akgül
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4292)

Abstract

Connected attribute filters are morphological operators widely used for their ability of simplifying the image without moving its contours. In this paper, we present a fast, versatile and easy-to-implement algorithm for grayscale connected attribute thinnings and thickennings, a subclass of connected filters for the wide range of non-increasing attributes. We show that our algorithm consumes less memory and is computationally more efficient than other available methods on natural images, for strictly identical results.

Keywords

Natural Image Cardiac Compute Tomography Morphological Operator Pruning Rule Resolution Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Acton, S.T., Mukherjee, D.P.: Scale space classification using area morphology. IEEE Transactions on Image Processing 9(4), 623–635 (2000)CrossRefGoogle Scholar
  2. 2.
    Ahuja, R.K., Magnanti, T.L., Orlin, J.B.: Network Flows. Prentice-Hall, Englewood Cliffs (1993)Google Scholar
  3. 3.
    Breen, E.J., Jones, R.: Attribute openings, thinnings and granulometries. Computer Vision and Image Understanding 64(3), 377–389 (1996)CrossRefGoogle Scholar
  4. 4.
    Caselles, V., Monasse, P.: Grain filters. J. of Math. Imaging and Vision 17(3), 249–270 (2002)MATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Crespo, J., Schafer, R., Serra, J., Gratin, C., Meyer, F.: The flat zone approach: A general low-level region merging segmentation method. Signal Processing 62(1), 37–60 (1998)CrossRefGoogle Scholar
  6. 6.
    Darbon, J., Akgül, C.B.: An efficient algorithm for attribute openings and closings. In: Proceedings of the 13th European Signal Processing Conference (EUSIPCO), Electronic proceedings (2005)Google Scholar
  7. 7.
    Dial, R., Glover, F., Karney, D., Klingman, D.: A computational analysis of alternative algorithms and labeling techniques for finding shortest path trees. Networks 9, 215–248 (1979)MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Guichard, F., Morel, J.M.: Mathematical morphology ”almost everywhere”. In: Proceedings of ISMM, pp. 293–303. Csiro Publishing (April 2002)Google Scholar
  9. 9.
    Heijmans, H.: Connected morphological operators for binary images. Computer Vision and Image Understanding 73(1), 99–120 (1999)MATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Heijmans, H.J.A.M.: Theoretical aspects of gray-level morphology. IEEE Trans. Pattern Anal. Mach. Intell. 13(6), 568–582 (1991)CrossRefGoogle Scholar
  11. 11.
    Hesselink, W.H.: Salembier’s min-tree algorithm turned into breadth first search. Information Processing Letters 88(5), 225–229 (2003)MATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Maragos, P., Ziff, R.D.: Threshold superposition in morphological image analysis systems. IEEE Trans. on Pattern Analysis and Machine Intelligence 12(5), 498–504 (1990)CrossRefGoogle Scholar
  13. 13.
    Meijster, A., Westenberg, M.A., Wilkinson, M.H.F.: Interactive shape preserving filtering and visualization of volumetric data. In: Proc. of the Conf. Comp. Signal Image Proc. (SIP), Kauai, Hawaii, USA, August 2002, pp. 640–643 (2002)Google Scholar
  14. 14.
    Meijster, A., Wilkinson, M.H.F.: A comparison of algorithms for connected set openings and closings. IEEE Trans. on PAMI 24(4), 484–494 (2002)Google Scholar
  15. 15.
    Meyer, F.: Levelings, image simplification filters for segmentation. Journal of Mathematical Imaging and Vision 20, 59–72 (2004)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Najman, L., Couprie, M.: Quasi-linear algorithm for the component tree. In: SPIE Symposium on Electronic Imaging, pp. 18–22 (2004)Google Scholar
  17. 17.
    Najman, L., Couprie, M.: Building the component tree in quasi-linear time. IEEE Transactions on Image Processing (accepted, 2006)Google Scholar
  18. 18.
    Salembier, P., Oliveras, A., Garrido, L.: Antiextensive connected operators for image and sequence processing. IEEE Trans. on Image Processing 7(4), 555–570 (1998)CrossRefGoogle Scholar
  19. 19.
    Salembier, P., Serra, J.: Flat zones filtering, connected operators and filters by reconstruction. IEEE Transactions on Image Processing 4, 1153–1160 (1995)CrossRefGoogle Scholar
  20. 20.
    Serra, J., Salembier, P.: Connected operators and pyramids. In: Proc. SPIE Image Algebra Math. Morphology, vol. 2030, pp. 65–76 (1993)Google Scholar
  21. 21.
    Soille, P.: Morphological Image Analysis Principles and Applications. Springer, Heidelberg (1999)MATHGoogle Scholar
  22. 22.
    Urbach, E.R., Wilkinson, M.H.F.: Shape-only granulometries and grey-scale shape filters. In: Proceedings of the International on Mathematical Morphology, pp. 305–314 (2002)Google Scholar
  23. 23.
    Vachier, C.: Morphological scale-space analysis and feature extraction. In: Proceedings of the ICIP 2001, October 2001, pp. 676–679 (2001)Google Scholar
  24. 24.
    Vincent, L.: Grayscale area openings and closings, their efficient implementation and applications. In: Proc. EURASIP Mathematical Morphology and Its Application to Signal Processing, pp. 22–27 (1993)Google Scholar
  25. 25.
    Wilkinson, M.H.F., Roerdink, J.B.T.M.: Fast morphological attribute operations using tarjan’s union-find algorithm. In: Proceedings of the ISMM, Palo Alto, CA, June 2000, pp. 311–320 (2000)Google Scholar
  26. 26.
    Wilkinson, M.H.F., Westenberg, M.A.: Shape preserving filament enhancement filtering. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 770–777. Springer, Heidelberg (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • David Lesage
    • 1
    • 4
  • Jérôme Darbon
    • 2
    • 5
  • Ceyhun Burak Akgül
    • 1
    • 3
  1. 1.École Nationale Supérieure des Télécommunications (ENST)ParisFrance
  2. 2.EPITA Research and Development Laboratory (LRDE)Le Kremlin-BicêtreFrance
  3. 3.Electrical and Electronics Engineering DepartmentBogazici UniversityBebek, IstanbulTurkey
  4. 4.in collaboration with Siemens Corporate ResearchPrincetonUSA
  5. 5.UCLA Mathematics DepartmentLos AngelesUSA

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